The proposed research will compare several scatter- and attenuation- correction methods on the basis of their effects on performance in quantitative imaging tasks using SPECT data. The imaging tasks to be considered include estimation of activity and size of spherical structures, estimation of activity within deep brain structures using boundary information derived from MR data, and classification of brain images into normal and Alzheimer's disease groups using cortical activity patterns and activity within deep brain structures. A measure of performance in each quantitative imaging task will be obtained for each correction method being evaluated. These measures will range from theoretical quantities, i.e., the Cramer-Rao lower bound on variance of parameter estimates, to experimentally measured quantities, i.e., standard error of activity values estimated from simulated or phantom images using maximum-likelihood or Bayesian techniques, to clinical quantities, i.e., are under the ROC curve of a statistical or neural network classifier Performance in these quantitative imaging tasks can be viewed as a measure of image quality and is, therefore, a relevant basis on which to compare scatter- and attenuation-correction methods. The proposed research will achieve several objectives beyond the major goal of comparison of correction methods. First, we will investigate the potential of nonuniform projection sampling to improve task performance by reducing noise levels at central locations. Second, we will address the use inn estimation of a priori information, and will evaluate a practical method of estimating deep structure activity using MR-derived boundary information. Progress in these first two areas is expected to lead to improved quantitation of activity in deep brain structures. Finally, we will apply neural networks, which are only beginning to be used in radiology and nuclear medicine, to the Alzheimer's disease- related classification tasks.